targetfev / AutoGluon ↗US Macroeconomic Consumption & Disposable Income
Annual macroeconomic benchmark tracking 31 US personal consumption expenditure and income categories over 64 years to forecast a 5-year medium-term horizon for fiscal planning and asset allocation.
Business Objective: US Macroeconomic Consumption & Disposable Income
01Business Context
Treasury desks, sovereign wealth funds, corporate strategic planning teams, and economic policy units require robust medium-term projections of consumer demand components (e.g., nondurables, clothing and footwear, services) and disposable income to steer multi-year capital deployment, liquidity buffers, and fiscal tax revenue forecasts.
02Analytical Objective
Accurately predict annual consumption expenditures and income aggregates across 31 individual macroeconomic categories over a 5-year forecast horizon.
03Economic & Decision Impact
Under-forecasting leads to mispriced sovereign bond yields, underfunded entitlement budgets, and inventory shortages in macro-sensitive sectors. Over-forecasting precipitates severe over-leverage, premature capital expenditure expansion, and stranded balance sheet debt during economic contraction phases.
Classical univariate statistical methods like AutoARIMA and damped Holt-Winters ETS perform reliably on smooth macro aggregates with clear secular drift, though they struggle around inflection points. Gradient boosted tree baselines (LightGBM) excel when cross-learning shared macroeconomic dynamics across all 31 series, while zero-shot pretrained foundation models (such as Mistral's Timestral, TimesFM, and MOIRAI) demonstrate strong generalization by transferring broad multi-decade macroeconomic priors directly in-context without overfitting small sample sizes.
Target & Feature Column Definitions
Exhaustive business definitions, measurement units, roles, and target variables across all 3 columns.
| Col | Variable / Feature Name | Role | Type | Unit / Scale | Description & Business Meaning |
|---|---|---|---|---|---|
| C | targetExpenditure / Income Value | TARGET | NUM | USD (Millions) | Real or nominal aggregate personal consumption expenditure or disposable income value for the corresponding category. |
| A | idExpenditure Category ID | SERIES ID | CAT | — | Categorical identifier representing the specific US personal consumption expenditure or disposable personal income component (e.g., clothing_and_footwear). |
| B | timestampYear Ending Timestamp | TIME | TIME | — | Annual calendar timestamp marking the end of each economic observation year (ISO 8601 format). |
Interactive Data Table
Explore rows, feature values, and target labels for US Macroeconomic Consumption & Disposable Income.
Foundation Models vs Traditional ML & Baselines (41 Methods)
Empirical evaluation directly from the AutoGluon fev benchmark suite evaluating zero-shot foundation models against deep learning, gradient-boosted trees, and statistical baselines across 100 evaluation tasks.
| Rank | Method / Model | Family | Organization | SQL Win Rate?Average win rate (%) against all competing models across the benchmark tasks under Scaled Quantile Loss. | SQL Skill?Normalized skill score relative to the Seasonal Naive baseline (0% = baseline, higher is better). | MASE Win Rate?Average win rate (%) under Mean Absolute Scaled Error. | Latency?Median end-to-end execution time per 100 time series in seconds. | Link |
|---|---|---|---|---|---|---|---|---|
| #1 | Timestral-209M | Foundation Model | Mistral AI | 87.7% | +50.4% | 86.0% | 0.51s | Repo ↗ |
| #2 | Timestral-123M (Carla Engine) | Foundation Model | Mistral AI | 86.8% | +50.2% | 85.0% | 0.32s | Repo ↗ |
| #3 | TimeRSI | Closed-Source API | GaTech & SamayLabs | 84.8% | +51.2% | 83.8% | 2512s | Repo ↗ |
| #4 | t0-gamma | Foundation Model | The Forecasting Company | 84.4% | +49.4% | 81.8% | 4.38s | Repo ↗ |
| #5 | Timestral-28M | Foundation Model | Mistral AI | 79.2% | +48.4% | 77.0% | 0.11s | Repo ↗ |
| #6 | timesfm-3 | Foundation Model | 78.5% | +49.0% | 77.0% | 3.41s | Repo ↗ | |
| #7 | ChakraTS | Baseline | Community | 78.2% | +48.1% | 79.5% | 14.22s | Repo ↗ |
| #8 | Lingjiang2.0 | Baseline | Community | 73.9% | +47.0% | 71.3% | 8.89s | Repo ↗ |
| #9 | Chronos-2 | Foundation Model | AWS | 70.4% | +47.3% | 68.4% | 0.84s | Repo ↗ |
| #10 | timeh2o-v1-tiny | Foundation Model | H2O.ai | 69.9% | +45.5% | 67.0% | 0.19s | Repo ↗ |
| #11 | Timestral-5M | Foundation Model | Mistral AI | 69.0% | +46.2% | 67.7% | 40ms | Repo ↗ |
| #12 | Toto-2.0-2.5B | Baseline | Community | 67.5% | +44.4% | 65.7% | 5.58s | Repo ↗ |
| #13 | t0-beta | Foundation Model | The Forecasting Company | 67.4% | +46.6% | 64.5% | 2.29s | Repo ↗ |
| #14 | Toto-2.0-1B | Baseline | Community | 66.4% | +44.4% | 65.5% | 2.48s | Repo ↗ |
| #15 | TiRex-2 | Foundation Model | NX-AI | 65.8% | +45.5% | 63.4% | 0.27s | Repo ↗ |
| #16 | Toto-2.0-313m | Baseline | Community | 65.1% | +44.2% | 63.5% | 1.06s | Repo ↗ |
| #17 | TiRex | Foundation Model | NX-AI | 58.0% | +42.6% | 54.1% | 0.24s | Repo ↗ |
| #18 | Toto-2.0-22m | Baseline | Community | 57.6% | +42.9% | 56.0% | 0.25s | Repo ↗ |
| #19 | TimesFM-2.5 | Baseline | Community | 54.6% | +42.2% | 53.5% | 1.87s | Repo ↗ |
| #20 | TabPFN-TS-3 | Foundation Model | Prior Labs | 52.4% | +43.1% | 51.3% | 235s | Repo ↗ |
| #21 | TS-ICL | Foundation Model | EDF-Lab | 51.2% | +43.1% | 49.8% | 2.49s | Repo ↗ |
| #22 | FlowState | Foundation Model | IBM | 49.3% | +39.9% | 49.9% | 2.29s | Repo ↗ |
| #23 | citras-fm | Foundation Model | Hitachi | 48.8% | +41.2% | 46.9% | 0.60s | Repo ↗ |
| #24 | Toto-1.0 | Baseline | Community | 47.5% | +40.8% | 45.9% | 22.35s | Repo ↗ |
| #25 | TabPFN-TS | Foundation Model | Prior Labs | 47.3% | +41.5% | 45.4% | 109s | Repo ↗ |
| #26 | Toto-2.0-4m | Baseline | Community | 46.2% | +40.7% | 43.6% | 0.20s | Repo ↗ |
| #27 | Moirai-2.0 | Baseline | Community | 41.9% | +39.3% | 40.5% | 0.35s | Repo ↗ |
| #28 | Chronos-Bolt | Foundation Model | AWS | 41.4% | +38.9% | 39.3% | 0.26s | Repo ↗ |
| #29 | TFT | Deep Learning | — | 31.7% | +32.2% | 29.2% | 829s | Repo ↗ |
| #30 | Stat. Ensemble | Baseline | Community | 27.5% | +20.7% | 30.6% | 147s | Repo ↗ |
| #31 | Sundial-Base | Foundation Model | Tsinghua University | 27.4% | +33.4% | 33.6% | 8.01s | Repo ↗ |
| #32 | PatchTST | Deep Learning | — | 26.8% | +30.1% | 25.1% | 664s | Repo ↗ |
| #33 | DeepAR | Deep Learning | — | 26.1% | +29.1% | 23.6% | 1113s | Repo ↗ |
| #34 | AutoARIMA | Statistical Baseline | — | 23.3% | +21.3% | 21.5% | 20.14s | Repo ↗ |
| #35 | AutoETS | Statistical Baseline | — | 20.3% | -26.8% | 22.5% | 3.48s | Repo ↗ |
| #36 | CatBoost | Tree Ensemble | — | 19.1% | +23.0% | 35.1% | 29.48s | Repo ↗ |
| #37 | LightGBM | Tree Ensemble | — | 17.8% | +21.0% | 33.1% | 3.03s | Repo ↗ |
| #38 | AutoTheta | Statistical Baseline | — | 15.6% | +5.5% | 19.8% | 3.28s | Repo ↗ |
| #39 | Seasonal Naive | Baseline | Community | 10.3% | 0.0% | 11.8% | 0.48s | Repo ↗ |
| #40 | Naive | Statistical Baseline | — | 6.7% | -45.4% | 10.9% | 0.47s | Repo ↗ |
| #41 | Drift | Statistical Baseline | — | 6.1% | -45.8% | 10.0% | 0.45s | Repo ↗ |
Variable Schema & Summary Distributions
Observed numerical ranges, category cardinalities, missing rates, and sample values across 1,984 rows.
| Col | Variable Name | Type | Missing | Distinct | Summary Stats / Distribution | Sample Values |
|---|---|---|---|---|---|---|
| C | targetTARGET | NUM | 0 | 1,976 | [115, 225873216] μ=3022699.3 σ=16222317 | 311275318850334301 |
| A | id | CAT | 0 | 31 | — | clothing_and_footwearclothing_and_footwearclothing_and_footwear |
| B | timestamp | CAT | 0 | 64 | — | 1960-12-31T00:00:001961-12-31T00:00:001962-12-31T00:00:00 |
Frequently Asked Questions: US Macroeconomic Consumption & Disposable Income
Common questions regarding the US Macroeconomic Consumption & Disposable Income dataset, machine learning task formulations, and in-browser tabular inference.
What is the forecasting horizon for US Macroeconomic Consumption & Disposable Income?
The benchmark specifies an annual forecast horizon of 5 years (H = 5), tailored for medium-term fiscal policy analysis, capital allocation, and multi-year corporate strategy.
Can I run zero-server forecasts on this dataset in Google Sheets?
Yes, Carla enables zero-server in-context time series forecasting directly in spreadsheets using foundation models such as Mistral's Timestral without exposing sensitive economic figures to third-party endpoints.
Which models perform best on US Macroeconomic Consumption & Disposable Income?
Zero-shot foundation models (such as Mistral's Timestral and MOIRAI) and pooled LightGBM models achieve top scores by leveraging cross-series dynamics, while AutoARIMA and damped Holt-Winters provide solid baselines on smooth aggregate series.
Test TabICLv2 on US Macroeconomic Consumption & Disposable Income Yourself
Open the pre-loaded Google Sheet and let Carla configure the target and task for local, zero-cloud tabular machine learning.
Launch Carla
Click once to open the spreadsheet and Carla side panel together.
Review the Setup
Carla selects the dataset target and task from this page automatically.
Evaluate & Predict
Run predictions and compute metrics with zero server uploads.
Provenance & Attribution
Hyndman, R.J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed). OTexts: Melbourne, Australia.